
import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Optional[int]    =	{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Dict          =			"""data2vec-audio"""

			def __init__(							self			:							List[Any]    ,						snake_case_			:							Any=32    ,						snake_case_			:							Optional[int]=768    ,						snake_case_			:							Union[str, Any]=12    ,						snake_case_			:							Dict=12    ,						snake_case_			:							int=3072    ,						snake_case_			:							Tuple="gelu"    ,						snake_case_			:							List[str]=0.1    ,						snake_case_			:							Any=0.1    ,						snake_case_			:							List[str]=0.1    ,						snake_case_			:							Tuple=0.0    ,						snake_case_			:							Any=0.1    ,						snake_case_			:							Optional[int]=0.1    ,						snake_case_			:							str=0.02    ,						snake_case_			:							Any=1e-5    ,						snake_case_			:							str="gelu"    ,						snake_case_			:							Any=(512, 512, 512, 512, 512, 512, 512)    ,						snake_case_			:							Dict=(5, 2, 2, 2, 2, 2, 2)    ,						snake_case_			:							Dict=(10, 3, 3, 3, 3, 2, 2)    ,						snake_case_			:							List[Any]=False    ,						snake_case_			:							Optional[int]=16    ,						snake_case_			:							Any=19    ,						snake_case_			:							int=5    ,						snake_case_			:							Any=0.05    ,						snake_case_			:							Optional[Any]=10    ,						snake_case_			:							Union[str, Any]=2    ,						snake_case_			:							int=0.0    ,						snake_case_			:							Optional[Any]=10    ,						snake_case_			:							Optional[Any]=0    ,						snake_case_			:							Any="sum"    ,						snake_case_			:							Dict=False    ,						snake_case_			:							Tuple=False    ,						snake_case_			:							List[Any]=256    ,						snake_case_			:							List[str]=(512, 512, 512, 512, 1500)    ,						snake_case_			:							Dict=(5, 3, 3, 1, 1)    ,						snake_case_			:							Dict=(1, 2, 3, 1, 1)    ,						snake_case_			:							List[Any]=512    ,						snake_case_			:							str=0    ,						snake_case_			:							Tuple=1    ,						snake_case_			:							Optional[Any]=2    ,						snake_case_			:							Optional[Any]=False    ,						snake_case_			:							Any=3    ,						snake_case_			:							Optional[int]=2    ,						snake_case_			:							Any=3    ,						snake_case_			:							Tuple=None    ,						**snake_case_			:							int    ,						):
							super().__init__(**snake_case_    ,						pad_token_id=snake_case_    ,						bos_token_id=snake_case_    ,						eos_token_id=snake_case_   )
							UpperCamelCase_:				Optional[Any]						=    hidden_size
							UpperCamelCase_:				Any						=    feat_extract_activation
							UpperCamelCase_:				int						=    list(snake_case_   )
							UpperCamelCase_:				Tuple						=    list(snake_case_   )
							UpperCamelCase_:				Optional[int]						=    list(snake_case_   )
							UpperCamelCase_:				Tuple						=    conv_bias
							UpperCamelCase_:				Dict						=    num_conv_pos_embeddings
							UpperCamelCase_:				Any						=    num_conv_pos_embedding_groups
							UpperCamelCase_:				Tuple						=    conv_pos_kernel_size
							UpperCamelCase_:				Optional[Any]						=    len(self.conv_dim   )
							UpperCamelCase_:				Dict						=    num_hidden_layers
							UpperCamelCase_:				List[Any]						=    intermediate_size
							UpperCamelCase_:				Union[str, Any]						=    hidden_act
							UpperCamelCase_:				Dict						=    num_attention_heads
							UpperCamelCase_:				str						=    hidden_dropout
							UpperCamelCase_:				Union[str, Any]						=    attention_dropout
							UpperCamelCase_:				Optional[int]						=    activation_dropout
							UpperCamelCase_:				Tuple						=    feat_proj_dropout
							UpperCamelCase_:				Dict						=    final_dropout
							UpperCamelCase_:				Any						=    layerdrop
							UpperCamelCase_:				Dict						=    layer_norm_eps
							UpperCamelCase_:				Optional[Any]						=    initializer_range
							UpperCamelCase_:				Union[str, Any]						=    vocab_size
							UpperCamelCase_:				str						=    use_weighted_layer_sum

							if (
							    (len(self.conv_stride   ) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
							    or (len(self.conv_dim   ) != self.num_feat_extract_layers)
							):
											raise ValueError(
											    """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
											    """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
											    f''' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'''
											    f''' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'''   )

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							UpperCamelCase_:				Tuple						=    mask_time_prob
							UpperCamelCase_:				Union[str, Any]						=    mask_time_length
							UpperCamelCase_:				Optional[Any]						=    mask_time_min_masks
							UpperCamelCase_:				List[Any]						=    mask_feature_prob
							UpperCamelCase_:				Union[str, Any]						=    mask_feature_length
							UpperCamelCase_:				str						=    mask_feature_min_masks

							# ctc loss
							UpperCamelCase_:				List[Any]						=    ctc_loss_reduction
							UpperCamelCase_:				Tuple						=    ctc_zero_infinity

							# adapter
							UpperCamelCase_:				Any						=    add_adapter
							UpperCamelCase_:				Optional[int]						=    adapter_kernel_size
							UpperCamelCase_:				Dict						=    adapter_stride
							UpperCamelCase_:				Optional[Any]						=    num_adapter_layers
							UpperCamelCase_:				Tuple						=    output_hidden_size or hidden_size

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							UpperCamelCase_:				int						=    classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							UpperCamelCase_:				int						=    list(snake_case_   )
							UpperCamelCase_:				Optional[Any]						=    list(snake_case_   )
							UpperCamelCase_:				List[Any]						=    list(snake_case_   )
							UpperCamelCase_:				Dict						=    xvector_output_dim




			@property
			def    lowerCAmelCase__						(							self			:							Dict   ):
							return math.prod(self.conv_stride   )








import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






